Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RAEe7AGjhCphfW3jm6KOb-HHTMFLktTf8V5vKT5mPDcD4/assertion>. }
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "Madrid Spain Mar May TotalOutdoorphysicalexercise" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "Mexico City Mexico Mar" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "availablefor Los Angeles" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "ECHAM HAM simulation" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "January February" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "ML model" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "aerosol precursor" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "aerosol radiative forcing reduction" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "air quality pollution" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "bibliography on air quality" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "collection of scientific article" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "demand variability" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "electricity demand value" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "emission reduction factor" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "energy industry" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "impact of covid 19" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "pandemic lockdown" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "reg AP emission inventory" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "threshold temperature value" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The datasetprovides a statistical summary for each of the air pollutant species, all air pollutants are converted toan Air Quality Index (AQI) with the U.S. Environmental Protection Agency (EPA) standard calculation." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The ECHAM HAM and CESM NoT ensembles allow more freedom for temperature adjustment." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Figure summarizes the main statistics (normalized meanbias, NMB; normalized root mean square error, NRMSE;and correlation, r) obtained from the comparison betweenmeasured and ML based electricity demand during the first months of for selected countries." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "In this study, ML models are used for predictingthe fluctuations of electricity demand based on the temper ature (and additional time features) assuming that temper ature is a strong driver of electricity demand (for heatingand air conditioning) However, temperature is obviously notthe only driver of electricity demand variability that can beinfluenced by various other factors (e.g. change of technol ogy, behaviour, regulation) In addition, the GBM modelsused in this study are non parametric, meaning that they can not extrapolate, i.e. predict electricity demand values out side the range of values used during the training phase." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Thepoorest performance was obtained in Finland (r.) dueto a strong negative anomaly (on average) of elec tricity demand in January February compared to pre vious years used for training." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The daily median AQI was used in this study for each of major cities." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The average temperature was not availablefor Los Angeles (LA) New York City (NYC) and Sydney, so the maximum temperature was used,as the maximum temperature is important for ozone formation." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The ECHAM HAM simulation set up is most similar to the CESM NoT ensemble described below." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Bibliography on air quality before, during and after lockdown." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The same emissions scenario (from Forster et al.) is run as for CESM using monthly emissions." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Collection of scientific articles and other communications related to the impact of COVID-19 pandemic lockdown on air quality pollution." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 TimeReference "winter" assertion.
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- 374d0d3a-4807-4925-be83-b9eea52356e3 TimeReference "the past winter" assertion.
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- 374d0d3a-4807-4925-be83-b9eea52356e3 description "Collection of scientific articles and other communications related to the impact of COVID-19 pandemic lockdown on air quality pollution." assertion.
- 116dfa4e-7918-403c-b4ef-1f995d91c775 description "The COVID-19 pandemic led to dramatic changes in economic activity in 2020. In this paper the authors use estimates of emission changes for 2020 in two Earth System Models (ESMs) to simulate the impacts of the COVID-19 economic changes." assertion.
- 1189b793-4040-49f4-bd1a-de0a03b83de8 description "The COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%." assertion.
- 6021e080-1951-495c-97d1-3fd1b4e3b0bb description "There is a strong body of evidence to show how air pollution affects different aspects of health at even lower concentrations than previously understood. But here’s what hasn’t changed: every year, exposure to air pollution is still estimated to cause millions of deaths and the loss of healthy years of life." assertion.
- 729c0b49-df8a-42de-8059-1a220c51f353 description "In this paper the authors quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Their estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality." assertion.
- 9c87dcef-0fd1-4225-8134-a79e4eaa2da3 description "Waste natural gas from industrial oil and gas fields could be a source of nitrogen dioxide and black carbon pollution, according to new research." assertion.
- a64fed5b-e924-4d23-b778-9832e56115bc description "This study has used, in a first stage, Sentinel-5P and CAMS service to analyze the air quality in the territory of Iberian Peninsula, as well as assess in detail major cities within the region (Lisbon, Porto and Madrid), for a period from January 2018 to April 2020. On a later stage, the data from Sentinel-3A and 3B allowed the analysis of water quality during the months March, April and May 2020, in the Portuguese coast. Regarding the air quality, NO2 and PM10 levels in the Iberian Peninsula were consistently lower compared to the same periods in the two past years." assertion.
- acb77822-d471-413d-a825-ad569e28f4dd description "In this work the authors investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. Part of the analysis employs a variety of machine learning tools." assertion.
- b37476c6-244e-4b80-8268-03c5fc1c7de6 description "Paris coronavirus. Man wearing a mask walking in front of the Eiffel Tower on the first day of Paris lock-down. Photo by The Paris Photographer on Unsplash" assertion.
- bc3d57ea-d82b-4261-9af7-24e949d1d5f5 description "Concawe has undertaken a city-level analysis to quantify the ways in which the Covid-19 lockdown measures have had an impact on air quality in Europe. This article presents the results of the analysis for particulate matter (PM 2.5 ), nitrogen dioxide (NO2 ) and ozone (O3 )." assertion.
- bc6922f1-debf-46ae-ba79-d574f6f5c064 description "To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2 , SO2 , CO, PM 2.5 and PM 10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown." assertion.
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- 374d0d3a-4807-4925-be83-b9eea52356e3 dateCreated "2021-12-20 11:04:49.605037+00:00" assertion.
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